1. Field of the Invention
This invention relates to the field of image recognition, and in particular to the use of genetic algorithms to determine which characteristics of an image are preferred for distinguishing among images for efficient and effective recognition and/or to determine a minimum number of different characteristics that are required for efficient and effective recognition.
2. Description of Related Art
Image recognition, and particularly face recognition, is becoming an increasingly popular feature in a variety of applications. Security systems use face recognition to grant or deny access to select individuals, or to sound an alarm when a particular person is recognized, or to continually track an individual as the individual travels amongst a plurality of people, and so on. In like manner, home automation systems are being configured to distinguish among residents of a home, so that the features of the system can be customized for each resident.
Most face recognition systems do not directly compare images to effect a recognition. Instead, each face is characterized using a predefined set of characteristic parameters, such as the ellipticity of the face, the spacing of the eyes, the shape of the chin, etc., and a search for a target face is based on a comparison of these characteristic parameters. These characteristic parameters are designed to facilitate a distinction between images of different faces, and a matching between different images of the same face. In this manner, a target image can be compared to a set of reference images, and, based on the characteristic parameters, the number of possible reference images that match the target image is reduced, preferably to one reference image that corresponds to the same face as in the target image. In a controlled environment, such as an entry vestibule with a security camera, the comparison of a target face to a library of authorized faces is a fairly straightforward process. An image of each of the authorized individuals will have been collected using an appropriate pose in a well lighted area. The person requesting entry to the secured facility will be instructed to stand at a certain point relative to the camera, to most closely match the environment in which the images of the authorized people were collected. Sufficient lighting is provided to facilitate a straightforward comparison process.
For most applications, however, requiring the target individual to pose is an unrealistic restriction. Most home occupants, for example, will not generally be agreeable to standing at a particular point in the home in order for the home automation system to recognize the occupant. Most security systems are designed to be unobtrusive, so as not to impede the normal course of business or travel, and would quickly become unusable if each person traveling to or through an area were required to stop and pose. Thus, in most applications, the target image or images will be obtained under less-than-ideal conditions, and will generally not correspond directly to the pose and orientation of the images in the library of images. In an image tracking system, wherein an image of a target is obtained from one scene, and then matched to images in subsequent scenes, neither the original image nor the subsequent images will be obtained under ideal conditions.
Because one or both of the images being compared for face recognition will generally be less-than-ideal, a rigid comparision of all the characteristic parameters that define each face will not generally be suitable. Some parameters will not be determinable due to the particular orientation of the face relative to the camera, or due to shadows introduced by less-than-ideal lighting conditions, or due to other environmental factors. A more complexing difficulty than the absence of a measure of a characteristic parameter, however, is the presence of a mis-measured characteristic parameter, also caused by capturing the image under less-than-ideal conditions. In a typical comparison of two images, some parameters will match, some parameters will not match, and some parameters will be absent.
Conventional face recognition systems require the ability to reach a match/no-match decision based on incomplete and often conflicting information. Generally, the developers of the system determine the particular parameters that are most instrumental in determining a match, and give these parameters more ‘weight’ in the match/no-match decision than other parameters. In some systems, combinations of parameters are given more ‘weight’ than their individual ‘weights’. For example, the ellipticity of the face and the spacing of the eyes may each have a certain significance in the match/no-match decision process, and an extra significance may be afforded to an image wherein there is a match of both the ellipticity and eye spacing. These decision rules are generally developed based on the trial-and-error analysis of hundreds of images. Obviously, the effectiveness of these decision rules distinguishes a successful face recognition system from unsuccessful systems, and considerable resources are expended to develop these decision rules.
Among competing face recognition systems, different performance characteristics will emerge. One system may be more successful than its competitors in matching faces in an outdoor environment; another system may be more successful in an indoor environment; and so on. Some systems may be particularly sensitive to camera angles, others may be sensitive to shadows. Although each vendor of a face recognition system would prefer to excel in all areas, the cost of developing rules for each environmental condition precludes the customization of the face recognition system for each potential environment. Also, in most applications, the face recognition system will be deployed in a variety of environments for a single customer. In such a case, the customer will generally select a system with sufficient overall performance, even though alternative systems provide better performance in select environments. That is, a system that provides mediocre indoor and outdoor performance will often be preferable to a system with excellent indoor performance and poor outdoor performance, or a system with poor indoor performance and excellent outdoor performance.